refactor(perception/lidar_apollo_instance_segmentation): rework parameters

perception/lidar_apollo_instance_segmentation/schema/lidar_apollo_instance_segmentation.json

@@ -0,0 +1,79 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "title": "Parameters for lidar_apollo_instance_segmentation",
+  "type": "object",
+  "definitions": {
+    "lidar_apollo_instance_segmentation": {
+      "type": "object",
+      "properties": {
+        "score_threshold": {
+          "type": "number",
+          "default": 0.8,
+          "description": "If the score of a detected object is lower than this value, the object is ignored."
+        },
+        "range": {
+          "type": "number",
+          "default": 60,
+          "description": "Half of the length of feature map sides.[m]."
+        },
+        "width": {
+          "type": "number",
+          "default": 640,
+          "description": "The grid width of feature map."
+        },
+        "height": {
+          "type": "number",
+          "default": 640,
+          "description": "The grid height of feature map."
+        },
+        "precision": {
+          "type": "string",
+          "default": "fp32"
+        },
+        "use_intensity_feature": {
+          "type": "boolean",
+          "default": "true",
+          "description": "The flag to use intensity feature of pointcloud."
+        },
+        "use_constant_feature": {
+          "type": "boolean",
+          "default": "false",
+          "description": "The flag to use direction and distance feature of pointcloud."
+        },
+        "target_frame": {
+          "type": "string",
+          "default": "base_link",
+          "description": "Pointcloud data is transformed into this frame."
+        },
+        "z_offset": {
+          "type": "number",
+          "default": -2.0,
+          "description": "z offset from target frame. [m]."
+        }
+      },
+      "required": [
+        "score_threshold",
+        "range",
+        "width",
+        "height",
+        "precision",
+        "use_intensity_feature",
+        "use_constant_feature",
+        "target_frame",
+        "z_offset"
+      ]
+    }
+  },
+  "properties": {
+    "/**": {
+      "type": "object",
+      "properties": {
+        "ros__parameters": {
+          "$ref": "#/definitions/lidar_apollo_instance_segmentation"
+        }
+      },
+      "required": ["ros__parameters"]
+    }
+  },
+  "required": ["/**"]
+}

perception/lidar_apollo_instance_segmentation/src/detector.cpp

@@ -28,16 +28,16 @@ LidarApolloInstanceSegmentation::LidarApolloInstanceSegmentation(rclcpp::Node *
   int range, width, height;
   bool use_intensity_feature, use_constant_feature;
   std::string onnx_file;
-  score_threshold_ = node_->declare_parameter("score_threshold", 0.8);
-  range = node_->declare_parameter("range", 60);
-  width = node_->declare_parameter("width", 640);
-  height = node_->declare_parameter("height", 640);
-  onnx_file = node_->declare_parameter("onnx_file", "vls-128.onnx");
-  use_intensity_feature = node_->declare_parameter("use_intensity_feature", true);
-  use_constant_feature = node_->declare_parameter("use_constant_feature", true);
-  target_frame_ = node_->declare_parameter("target_frame", "base_link");
-  z_offset_ = node_->declare_parameter<float>("z_offset", -2.0);
-  const auto precision = node_->declare_parameter("precision", "fp32");
+  score_threshold_ = node_->declare_parameter<double>("score_threshold");
+  range = node_->declare_parameter<int>("range");
+  width = node_->declare_parameter<int>("width");
+  height = node_->declare_parameter<int>("height");
+  onnx_file = node_->declare_parameter<std::string>("onnx_file");
+  use_intensity_feature = node_->declare_parameter<bool>("use_intensity_feature");
+  use_constant_feature = node_->declare_parameter<bool>("use_constant_feature");
+  target_frame_ = node_->declare_parameter<std::string>("target_frame");
+  z_offset_ = node_->declare_parameter<float>("z_offset");
+  const auto precision = node_->declare_parameter<std::string>("precision");
 
   trt_common_ = std::make_unique<tensorrt_common::TrtCommon>(
     onnx_file, precision, nullptr, tensorrt_common::BatchConfig{1, 1, 1}, 1 << 30);